Elsey Jack L, Miller Eric L, Christ John A, Abriola Linda M
Department of Civil and Environmental Engineering, Tufts University, Medford, MA 02155, USA.
Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA.
J Contam Hydrol. 2024 Mar;262:104323. doi: 10.1016/j.jconhyd.2024.104323. Epub 2024 Feb 20.
While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2-6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates.
尽管已有数十项研究试图估算微生物还原脱氯的莫诺德动力学参数，但文献中公布的值相差2至6个数量级。这种缺乏一致性的情况部分可归因于实验设计和参数估计技术的局限性。为了解决这些问题，采用哈密顿蒙特卡罗方法在各种实验条件下生成了超过100万组逼真的模拟微观世界数据。然后，这些数据被用于模型拟合实验，使用多种参数估计算法来确定莫诺德动力学参数。对传统的一式三份微观世界数据的分析产生了具有高共线性特征的参数估计值，导致估计精度和准确性较差。此外，通过常用的经典回归分析技术计算的置信区间包含真实参数值的频率远低于其标称置信水平。结果表明，使用一种替代实验设计，其分析次数与传统实验相同，但由初始氯化乙烯浓度不同的微观世界组成，可使参数不确定性降低一个数量级。一种可以在典型个人计算机上运行的 metropolis 算法被证明能返回更可靠的参数区间估计值。
